What Sentiment Radar monitors
Sentiment Radar focuses exclusively on sources you don’t control — the external web mentions that feed into AI training data and inform the citations AI systems produce when recommending or describing brands in your category. These sources matter because AI doesn’t evaluate your brand only by what your website says. When generating a response, an LLM’s understanding is shaped by the aggregate signal across all sources it has processed. A brand with strong owned content but overwhelmingly negative external mentions will often be described with qualifications, caveats, or omitted in favor of competitors with cleaner reputations.The five reputation channels
Sentiment Radar organizes external mentions into five channels, each reflecting a distinct audience and type of influence signal.Customer reviews
Mentions from product review platforms, app stores, G2, Capterra, Trustpilot, and similar sites. These carry high weight because they represent direct buyer experience and are frequently cited by AI when answering evaluation or comparison prompts.
Employee reviews
Mentions from Glassdoor, Indeed, Blind, and similar employer review platforms. While not directly relevant to product decisions, AI uses these as legitimacy and stability signals when assessing brand credibility.
Social platforms
Mentions from LinkedIn, Reddit, X (formerly Twitter), and other public social channels. These reflect real-time community sentiment and often carry emerging narratives before they appear in formal coverage.
Trust and legitimacy signals
Mentions from business directories, accreditation bodies, industry associations, and fact-checking sources. These are strong anchors for AI entity confidence — they signal that your brand is recognized by authoritative third parties.
News coverage
Mentions from news publications and industry media. News citations are among the highest-authority external signals AI systems use. A positive news mention contributes significantly to citation likelihood; a critical one can introduce persistent qualifications into AI responses.
Interpreting your sentiment data
The Sentiment Radar dashboard shows you a channel-by-channel breakdown of sentiment polarity (positive, neutral, negative) alongside the volume of mentions detected in each channel over your selected time range. When reading the report, focus on these areas: Where positive sentiment is strongest. Channels showing high positive mention volume are working for you. They’re contributing favorable signals to the external narrative AI draws on. Identify what’s driving positive mentions in those channels and look for ways to replicate that on channels where your score is weaker. Where criticism concentrates. A channel with a disproportionate share of negative sentiment is a liability. AI systems that cite sources in that channel will absorb that tone into their understanding of your brand. A high volume of critical customer reviews, for example, can cause AI to add unprompted caveats when recommending your product. Which narratives are spreading. The trend view shows whether sentiment in a channel is improving, stable, or deteriorating over time. A sudden spike in negative social mentions often precedes a shift in AI tone within days to weeks, as AI systems update their retrieval indexes. Gaps in trust signals. If the trust and legitimacy channel shows low mention volume, your brand lacks the third-party validation anchors that AI systems use to establish entity credibility. This is a common but fixable gap.Sentiment Radar captures publicly accessible external mentions. It does not monitor private or authenticated content, internal communications, or paywalled sources.
The connection between external sentiment and AI perception
AI systems are citation-aware. When a language model generates a response that describes your brand, it’s drawing on a learned representation built from everything it has processed about you — including external sources with strong positive or negative tone signals. This means that external sentiment affects AI perception through two mechanisms:- Training data influence — the tone of external content included in an LLM’s training corpus shapes how the model describes your brand by default, without any live retrieval happening.
- Live retrieval influence — for AI systems that perform real-time web retrieval (like ChatGPT with Browse enabled), negative or credibility-undermining sources can be pulled into responses directly, sometimes as explicit citations.
Acting on sentiment signals
Use the following approaches to address the issues Sentiment Radar surfaces. For negative customer review signals: Respond promptly and constructively to negative reviews on high-visibility platforms. AI systems index tone patterns, not just individual reviews — a consistent pattern of resolved complaints signals responsiveness more strongly than isolated positive reviews. For weak trust and legitimacy signals: Apply to relevant industry directories, accreditation programs, and business associations that are authoritative in your category. Request reviews or mentions from partners and analysts who publish on indexed platforms. Each verified third-party mention strengthens AI’s entity confidence in your brand. For negative or thin news coverage: Consider a proactive PR effort — data studies, expert commentary, or product announcements that give journalists citable, neutral-to-positive material. Even a single high-authority news mention can shift the tone balance in AI responses for that topic area. For emerging social narratives: Monitor the sentiment trend view weekly. When a new negative narrative begins spreading on social platforms, address it in public-facing content before it has time to propagate to higher-authority channels or get indexed into AI retrieval systems.Sentiment Radar monitors and reports on external signals. Improving those signals requires action in the channels themselves — publishing responses, generating new coverage, or earning new trust-signal mentions. GenRank tracks the results of those efforts automatically as new data is captured.